def main(opts, args): sys.stdout.write('dhad.gen.subset ... ') sys.stdout.flush() parsed = parse_args(args) datatype = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] mode = modes[0] modekey, sign = get_modekey_sign(mode) input_label = label.split('/')[0] children = label.split('/')[1] infile = get_rootfile(datatype, mode, input_label) outfile = get_rootfile(datatype, mode, label, opt='Create') if opts.test: outfile += '.test' sys.stdout.write('Input rootfile: %s \n' % infile) sys.stdout.write('Output rootfile: %s \n' % outfile) pt = add_rootfile(infile) nselected, ntotal = copy_events(pt, outfile, sign, children, opts.test) sys.stdout.write(' selected %s out of %s.\n' % (nselected, ntotal)) sys.stdout.flush()
def main(opts, args): tools.set_root_style(stat=1, grid=0) parsed = parse_args(args) datatype = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] figpath = os.path.join(attr.figpath, label, 'trkmtm') for mode in modes: modekey = tools.get_modekey(mode) sname = attr.modes[modekey]['sname'].lower() if sname == 'kpipi0': draw_momenta_kpipi0(datatype, mode, label, test=opts.test) elif sname == 'k3pi': draw_momenta_k3pi(datatype, mode, label, test=opts.test) elif sname == 'kpipi': draw_momenta_kpipi(datatype, mode, label, test=opts.test) elif sname == 'kpipipi0': draw_momenta_kpipipi0(datatype, mode, label, test=opts.test) elif sname == 'kspipi0': draw_momenta_kspipi0(datatype, mode, label, test=opts.test) elif sname == 'ks3pi': draw_momenta_ks3pi(datatype, mode, label, test=opts.test) elif sname == 'kkpi': draw_momenta_kkpi(figpath, datatype, mode, label, test=opts.test) else: raise NameError(sname)
def main(opts, args): parsed = parse_args(args) datatype = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] for mode in modes: if opts.set and opts.set == 'interact': if tag == 'single' or tag == 'double': single_tag_mode(datatype, mode, label, opts.test) else: raise ValueError(tag) else: submit_batch_job(datatype, tag, mode, label, opts)
def main(): alpha, num_iterations, animate = tools.parse_args() data = pd.read_csv("data.csv") X = np.array(data['km'], dtype='float64') y = np.array(data['price'], dtype='float64') X_norm, mu, sigma = feature_normalize(X) theta = np.array([0, 0], dtype='float64') theta, J_history, weight_list = fit(X_norm, y, theta, alpha, num_iterations) if animate == True: v.visualize_animate(weight_list, X_norm, X, y, num_iterations) else: v.visualize_cost(J_history) v.visualize_regression(theta, X_norm, X, y) tools.save_theta(theta, mu, sigma)
def main(): # parse a set of parameters args = parse_args() # parameters image_height, image_width = args.input_shape[0], args.input_shape[1] # create directory tree dir_dict = {} dir_dict = {'root' : ['logs', 'data'],\ 'data' : ['h5'],\ 'logs' : ['nn_logs'] } dir_create(args.dir, dir_dict) # Load and split data set train_lines, validation_lines = data_load(args) # Build a model model = model_build(args) # Train and save the model history = model_train(args, model, train_lines, validation_lines)
def main(opts, args): if args[0] == 'crossfeeds': import crossfeeds crossfeeds.entry(opts, args[1:]) return if args[0] == 'events': import events events.main(opts, args[1:]) return if args[0] == 'backgrounds': import backgrounds backgrounds.main(opts, args[1:]) return if args[0] == 'var': import var var.main(opts, args[1:]) return parsed = parse_args(args) datatype = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] for mode in modes: if opts.set and opts.set == 'interact': if tag == 'single': single_tag_mode(datatype, mode, label, opts.test) elif tag == 'double': double_tag_mode(datatype, mode, label, opts.test) else: raise ValueError(tag) else: submit_batch_job(datatype, tag, mode, label, opts)
"rbf": True } # easy check to validate -k option # -- parse command line and convert to convenience variables ----------------------------------------------- flags = { # define possible flags and the default: map key, type, default value "-a": ("pr_ave", str_flag, "binary"), "-N": ("normalise", bool_flag, True), "-f": ("classfirst", bool_flag, False), "-k": ("kernel_type", str_flag, "linear"), "-l": ("learn_curve", bool_flag, True), # generate learning curve unless -l given "-s": ("rstate", int_flag, 100), "-t": ("tsize", int_flag, 30) } opts = {} # map where option values or defaults come back pparms = parse_args(flags, opts, "training-file [testing-file]") if pparms == None or len(pparms) < 1: printf("missing filename on command line\n") sys.exit(1) kernel_type = opts["kernel_type"] if not kernel_type in valid_ktypes: printf("[FAIL] %s is not a valid kernel type for -k parameter\n", kernel_type) printf("\tvalid types are: linear, polynomial, or RBF\n") os.exit(1) filen = pparms[0] # raw data file manditory first parameter pr_ave = opts["pr_ave"] gen_lc = opts["learn_curve"]
def main(opts, args): if args[0] == 'backgrounds': import backgrounds backgrounds.main(opts, args[1:]) return if args[0] == 'brs': import brs brs.main(opts, args[1:]) return if args[0] == 'compare': import compare compare.main(opts, args[1:]) return if args[0] == 'cbx': import cbx cbx.main(opts, args[1:]) return if args[0] == 'crossfeeds': import crossfeeds crossfeeds.main(opts, args[1:]) return if args[0] == 'evt': import evt evt.main(opts, args[1:]) return if args[0] == 'fun': import fun fun.main(opts, args[1:]) return if args[0] == 'kkmass': import kkmass kkmass.main(opts, args[1:]) return if args[0] == 'kpimass': import kpimass kpimass.main(opts, args[1:]) return if args[0] == 'trim': import trim trim.main(opts, args[1:]) return if args[0] == 'trkmtm': import trkmtm trkmtm.main(opts, args[1:]) return if args[0] == 'trkmtm1': import trkmtm1 trkmtm1.main(opts, args[1:]) return if args[0] == 'trkmtm2': import trkmtm2 trkmtm2.main(opts, args[1:]) return if args[0] == 'var': import var var.main(opts, args[1:]) return figname = '_'.join(args).replace('/', '_') parsed = parse_args(args) dt_type = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] sqrt = False if opts.set and opts.set == 'sqrt': sqrt = True linr = False if opts.set and opts.set == 'linr': linr = True org = UserFile() org.append(attr.fig_web_header) for mode in modes: if mode == 'double_all_d0s' or mode == 'double_all_dps': modekey = mode else: modekey = get_modekey(mode) if sqrt: sqrt_fig_mode(tag, dt_type, modekey, label) sys.stdout.write('Creating %s ...' % mode) sys.stdout.flush() msg = create_fig_mode(tag, dt_type, modekey, label) sys.stdout.write(' OK.\n') org.append(msg) org.append(attr.fig_web_footer) figlabel = label.split('/')[0] figname = figname.replace(figlabel, '') figname = figname.replace('__', '_') orgname = figname + '.org' orgname = orgname.replace('_.org', '.org') orgpath = os.path.join(attr.figpath, figlabel) orgfile = tools.check_and_join(orgpath, orgname) verbose = opts.verbose if opts.test: verbose = 1 org.output(orgfile, verbose=verbose) orglink = '[[./fig/%s/%s][figure]]' % (figlabel, orgname) sys.stdout.write('\n%s\n\n' % orglink) if opts.test: return tools.org_export_as_html(orgfile)
def main(opts, args): if args[0] == 'backgrounds': import backgrounds backgrounds.main(opts, args[1:]) return if args[0] == 'brs': import brs brs.main(opts, args[1:]) return if args[0] == 'compare': import compare compare.main(opts, args[1:]) return if args[0] == 'cbx': import cbx cbx.main(opts, args[1:]) return if args[0] == 'crossfeeds': import crossfeeds crossfeeds.main(opts, args[1:]) return if args[0] == 'evt': import evt evt.main(opts, args[1:]) return if args[0] == 'fun': import fun fun.main(opts, args[1:]) return if args[0] == 'kkmass': import kkmass kkmass.main(opts, args[1:]) return if args[0] == 'kpimass': import kpimass kpimass.main(opts, args[1:]) return if args[0] == 'trim': import trim trim.main(opts, args[1:]) return if args[0] == 'trkmtm': import trkmtm trkmtm.main(opts, args[1:]) return if args[0] == 'trkmtm1': import trkmtm1 trkmtm1.main(opts, args[1:]) return if args[0] == 'trkmtm2': import trkmtm2 trkmtm2.main(opts, args[1:]) return if args[0] == 'var': import var var.main(opts, args[1:]) return figname = '_'.join(args).replace('/', '_') parsed = parse_args(args) dt_type = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] sqrt = False if opts.set and opts.set == 'sqrt': sqrt = True linr = False if opts.set and opts.set == 'linr': linr = True org = UserFile() org.append(attr.fig_web_header) for mode in modes: if mode == 'double_all_d0s' or mode == 'double_all_dps' : modekey = mode else: modekey = get_modekey(mode) if sqrt: sqrt_fig_mode(tag, dt_type, modekey, label) sys.stdout.write('Creating %s ...' % mode) sys.stdout.flush() msg = create_fig_mode(tag, dt_type, modekey, label) sys.stdout.write(' OK.\n') org.append(msg) org.append(attr.fig_web_footer) figlabel = label.split('/')[0] figname = figname.replace(figlabel, '') figname = figname.replace('__', '_') orgname = figname+'.org' orgname = orgname.replace('_.org', '.org') orgpath = os.path.join(attr.figpath, figlabel) orgfile = tools.check_and_join(orgpath, orgname) verbose = opts.verbose if opts.test: verbose = 1 org.output(orgfile, verbose=verbose) orglink = '[[./fig/%s/%s][figure]]' %(figlabel, orgname) sys.stdout.write('\n%s\n\n' % orglink) if opts.test: return tools.org_export_as_html(orgfile)
pass def test_all(self): pass if __name__ == "__main__": args = sys.argv[1:] if len(args) == 0: print "Running unittests." unittest.main() [input_args, output_args] = tools.parse_args( args, [["-in", "-i", "-input", "--input"], ["-out", "-o", "-output", "--output"]] ) input = input_fname = None output = output_fname = None if len(input_args) == 0: print "error: no input files understood." quit() while len(output_args) < len(input_args): output_args.append("") for k, ofname in enumerate(output_args): if ofname in ["", None]: ifname = input_args[k]
self.plot_scatter(lat, lon) lat, lon = self.coor_vgt.get_row_by_name(pt)[['LATITUDE', 'LONGITUDE']].values.T self.plot_scatter(lat, lon) def show(self): plt.gca().axis('equal') ## Set colorscale identical for all imshow vmin, vmax = self.imshow_clim for im in self.imshow: im.set_clim(vmin=vmin, vmax=vmax) #self.fig.colorbar(im, ax=self.ax) self.fig.colorbar(im, ax=self.ax) plt.show() if __name__ == '__main__': kwargs = tools.parse_args() m = Main() #m.plot_grid(**kwargs) # input args: f1=<path>:f2=<path> m.extract_land_mask(**kwargs) # input args: f1=<path> #m.plot_extract_coor(**kwargs) # input args: csvcoorfile, f1, f2 #m.show()
print '' sys.stdout.write("done.\n") return tex_lines if __name__ == "__main__": args = sys.argv[1:] [input_args, output_args ] = tools.parse_args(args, [['-in','-i','-input','--input'], ['-out','-o','-output','--output'] ] ) input = input_fname = None output = output_fname = None if len(input_args) == 0: print "error: no input files understood." quit() while len(output_args) < len(input_args): output_args.append('') for k,ofname in enumerate(output_args): if ofname in [ '', None ]: ifname = input_args[k] (name, in_ext) = os.path.splitext(ifname)
def main(opts, args): if args[0] == 'crossfeeds': import crossfeeds crossfeeds.main(opts, args[1:]) return if args[0] == 'backgrounds': import backgrounds backgrounds.main(opts, args[1:]) return if args[0] == 'sidebands': import sidebands sidebands.main(opts, args[1:]) return if args[0] == 'kkmass': import kkmass kkmass.main(opts, args[1:]) return if args[0] == 'kkmass2': import kkmass2 kkmass2.main(opts, args[1:]) return if args[0] == 'kpimass': import kpimass kpimass.main(opts, args[1:]) return ROOT.gROOT.SetBatch(1) ROOT.gROOT.SetStyle("Plain") parsed = parse_args(args) dt_type = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] if modes == ['double_all_d0s']: mode = modes[0] if opts.set and opts.set == 'interact': double_all_d0s(dt_type, mode, label, test=opts.test) else: pyline = 'import fit; fit.%s("%s", "%s", "%s", test=%s)' % ( mode, dt_type, mode, label, opts.test) bash_file_name = 'fit-mbc.sh' bash_file = create_bash_file_pyline(opts, label, dt_type, pyline, bash_file_name) logfile = tools.set_file('txt', dt_type, mode, tag, prefix='dir_' + label, extbase=attr.logpath) qjobname = 'fitd0s' tools.qsub_jobs(logfile, qjobname, bash_file, opts.test) return if modes == ['double_all_dps']: mode = modes[0] if opts.set and opts.set == 'interact': double_all_dps(dt_type, mode, label, test=opts.test) else: pyline = 'import fit; fit.%s("%s", "%s", "%s", test=%s)' % ( mode, dt_type, mode, label, opts.test) bash_file_name = 'fit-mbc.sh' bash_file = create_bash_file_pyline(opts, label, dt_type, pyline, bash_file_name) logfile = tools.set_file('txt', dt_type, mode, tag, prefix='dir_' + label, extbase=attr.logpath) qjobname = 'fitdps' tools.qsub_jobs(logfile, qjobname, bash_file, opts.test) return for mode in modes: if opts.set and opts.set == 'interact': if tag == 'single': single_tag_mode(dt_type, mode, label, interact=True, test=opts.test) elif tag == 'double': double_tag_mode(dt_type, mode, label, interact=True, test=opts.test) else: raise ValueError(tag) continue else: submit_batch_job(dt_type, tag, mode, label, opts)
def main(opts, args): if args[0] == 'crossfeeds': import crossfeeds crossfeeds.main(opts, args[1:]) return if args[0] == 'backgrounds': import backgrounds backgrounds.main(opts, args[1:]) return if args[0] == 'sidebands': import sidebands sidebands.main(opts, args[1:]) return if args[0] == 'kkmass': import kkmass kkmass.main(opts, args[1:]) return if args[0] == 'kkmass2': import kkmass2 kkmass2.main(opts, args[1:]) return if args[0] == 'kpimass': import kpimass kpimass.main(opts, args[1:]) return ROOT.gROOT.SetBatch(1) ROOT.gROOT.SetStyle("Plain") parsed = parse_args(args) dt_type = parsed[0] tag = parsed[1] modes = parsed[2] label = parsed[3] if modes == ['double_all_d0s']: mode = modes[0] if opts.set and opts.set == 'interact': double_all_d0s(dt_type, mode, label, test=opts.test) else: pyline = 'import fit; fit.%s("%s", "%s", "%s", test=%s)'% ( mode, dt_type, mode, label, opts.test) bash_file_name = 'fit-mbc.sh' bash_file = create_bash_file_pyline(opts, label, dt_type, pyline, bash_file_name) logfile = tools.set_file('txt', dt_type, mode, tag, prefix='dir_'+label, extbase=attr.logpath) qjobname = 'fitd0s' tools.qsub_jobs(logfile, qjobname, bash_file, opts.test) return if modes == ['double_all_dps']: mode = modes[0] if opts.set and opts.set == 'interact': double_all_dps(dt_type, mode, label, test=opts.test) else: pyline = 'import fit; fit.%s("%s", "%s", "%s", test=%s)'% ( mode, dt_type, mode, label, opts.test) bash_file_name = 'fit-mbc.sh' bash_file = create_bash_file_pyline(opts, label, dt_type, pyline, bash_file_name) logfile = tools.set_file('txt', dt_type, mode, tag, prefix='dir_'+label, extbase=attr.logpath) qjobname = 'fitdps' tools.qsub_jobs(logfile, qjobname, bash_file, opts.test) return for mode in modes: if opts.set and opts.set == 'interact': if tag == 'single': single_tag_mode(dt_type, mode, label, interact=True, test=opts.test) elif tag == 'double': double_tag_mode(dt_type, mode, label, interact=True, test=opts.test) else: raise ValueError(tag) continue else: submit_batch_job(dt_type, tag, mode, label, opts)